Articles containing the keyword 'forest pathology'

The aim of the study was to find out what are the causes of damage in different parts of the trees and the frequency of different kinds of injuries. Sample plots were studied in over 80-year old forests in mineral soil sites and peatlands. All the trees over 1.5 m high were felled in the sample plots and the stem injuries were studied. The structure of the stand and the crown classes were recorded. The proportion of undamaged trees was largest in in dominant and codominant trees and increased towards the better forest site types. The typical injuries are listed for Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L). H. Karst.) and Betula sp. stands. The injuries were divided in inner and outer form defects and injuries, and defined in more detail by the part of the stem and tree species. Defects caused by decay were analyzed separately.

Healing over of injuries was faster in the better sites. Form defects and other injuries were more common in birch stands than in Scots pine and Norway spruce stands. Decay was most common in birch stands. The pine stands were the healthiest, followed by spruce stands. Fire wound were most usual in pine, butt rot for spruce, and crooks and general decay for birch.

The PDF includes a summary in German.

• Tikka, E-mail: pt@mm.unknown

Pathogenic wood decay fungi such as species of Heterobasidion are some of the most serious forest pathogens in Europe, causing rot of tree boles and loss of growth, with estimated economic losses of eight hundred million euros per year. In conifers with low resinous heartwood such as species of Picea and Abies, these fungi are commonly confined to heartwood and thus external infection signs on the bark or foliage of trees are normally absent. Consequently, determining the extent of disease presence in a forest stand with field surveys is not practical for guiding forest management decisions such as optimal rotation time. Remote sensing technologies such as airborne laser scanning and aerial imagery are already used to reduce the reliance on fieldwork in forest inventories. This study aimed to use remote sensing to detect rot in spruce (Picea abies L. Karst.) forests in Norway. An airborne hyperspectral imager provided information for classifying the presence or absence of rot in a single-tree-based framework. Ground reference data showing the presence of rot were collected by harvest machine operators during the harvest of forest stands. Random forest and support vector machine algorithms were used to classify the presence and absence of rot. Results indicate a 64% overall classification accuracy for presence-absence classification of rot, although additional work remains to make the classifications usable for practical forest management.

• Allen, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: benjamin.allen@nmbu.no
• Dalponte, Department of Sustainable Agro-ecosystems and Bioresources, Research and Innovation Centre, Fondazione E. Mach, Via E. Mach 1, 38098 San Michele all’Adige (TN), Italy E-mail: michele.dalponte@fmach.it
• Hietala, Norwegian Institute of Bioeconomy Research, Innocamp Steinkjer, Skolegata 22, NO-7713 Steinkjer, Norway E-mail: Ari.Hietala@nibio.no
• Ørka, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: hans-ole.orka@nmbu.no
• Næsset, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: erik.naesset@nmbu.no
• Gobakken, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: terje.gobakken@nmbu.no